Sorry I hide my true identity but I'm a physicist/engineer, native contrarian and idea generator. SA photo reflects my soul quite well. I am an eclectic dividend investor with motto "In God We Trust, All Others Pay Cash" applied to companies I invest in.
I like to read /and read a lot - did you look on my photo 8-)? / including popular and academic investment books and papers. After 200+ books I concluded that many (but not all) finance academics failed to delivery a good science because they usually are more concerned about match between their models and limited (in time and place) data-sets than about underlying assumptions of their models. On another hand, finance practitioners such as fund managers ...More have different goals than I (for example, they want to outperform or replicate market each single year while my goal is to have smooth income from my investment and I don't worry to underperform in a bull market) and to some extend more limited in their choices than I (for example, with micro- and nano-cap stocks). It gives a chance for me as amateur investor to compete successfully with professionals in niche strategies such as dividend investment (see http://seekingalpha.com/instablog/725729-sds-seductive-dividend-stocks/266502-why-i-m-a-dividend-zealot-jan-31-2012).
My real portfolio consists of more than 100 dividend growth (DG) and high dividend yield (HY) stocks of USA and foreign companies I cherry-picked from the end of XX century. Some of these stocks with significant capital appreciation might even produce "free money" (see http://seekingalpha.com/instablog/725729-sds-seductive-dividend-stocks/352651-a-fool-and-free-money-27-feb-2012). I also maintain artificial so-called "poor"folio of dividend stocks I use for self-education about market.
I understand that DGI is mostly trust in company's Board of Directors consistency and that HYI is mostly disagreement with market sentiment but both styles fit my goals and mentality,
My investor edges are
i) critical scientific approach (used in natural science rather than in liberal sciences) to finance academics ideas and strong selection between useful and worthless findings;
ii) quite predictable proprietary model of dividend reductions forecast in near future (couple years) that I delivered from mix of hardware engineering ideas and physics concepts with finance data and behavior signals that allows me to sell stocks before such unpleasant event;
iii) independence in time frames and market exposures forbidden for many finance practitioners;
iv) analyses of companies that are too small for institutional investors.
I have couple excellent ideas in dividend investing I'd like to capitalize, so serious requests are welcome.
I rather put my thoughts and ideas in SA Instablog and comments than in articles (I'm pretty busy/lazy/English-incompetent to perfect an article) but in all cases all standard disclaimers are applied. One of good things I have learned in Intel, that decision should be data driven. So I try to supply my ideas and thoughts with most relevant data. I love old Russian writer and dramatist Anton Chekhov principle "Brevity is the sister of talent" and think it is even more important nowadays with ocean of information in front of any investor. So, I try to follow this principle in my SA instablog and comments but please remember that "If I have more time, I would have written shorter".
Being a scientific journals referee I have a bad habit to find few weak points in almost any manuscript, so I probably too critical in some comments but I hope the article authors excuse me. I prefer communicate via SA email rather than inside comments (I usually turn off "Track new comments on this article" feature SA has). So send me a SA email if you have a question or would like to discuss my point of view.
I like to read /and read a lot - did you look on my photo 8-)? / including popular and academic investment books and papers. After 200+ books I concluded that many (but not all) finance academics failed to delivery a good science because they usually are more concerned about match between their models and limited (in time and place) data-sets than about underlying assumptions of their models. On another hand, finance practitioners such as fund managers ...More